Search Results (21,382)

Fast-charging sites for electric buses create short-duration high-power demand that can increase grid-capacity requirements and operating costs. This paper assesses the economic performance of second-life battery energy storage systems (BESS) at an operational electric-bus fast-charging site in Maribor, Slovenia. Using measured charging-demand data, this study compares baseline operation with three implementation scenarios: a small-scale BESS, a full-scale BESS, and a full-scale BESS integrated with photovoltaic (PV) generation. A 12-year scenario-based cost–benefit assessment (CBA) considers investment, grid-electricity, and operation and maintenance costs under the applicable network tariff. The results show that BESS-only configurations are not economically justified under the analysed conditions. Importantly, even a substantial temporary reduction in grid demand from approximately 150 kW to 60 kW produces only limited cost savings, demonstrating that technically effective peak shaving does not necessarily translate into economic viability. The strongest economic performance is achieved by combining BESS with the annual-demand-matched 92.4 kWp PV system, which substantially reduces annual grid-electricity demand and operating costs. Nevertheless, this configuration does not reach break-even within the 12-year assessment period; under the reference assumption of a 5% annual increase in grid-electricity costs, extrapolation indicates break-even in approximately year 18. The findings demonstrate that the economic value of second-life BESS at fast-charging sites depends not only on peak-shaving capability but on its interaction with tariff structure, grid conditions, and local renewable generation. Full article

With the ongoing advancement of China’s “dual carbon” strategy, wind power and photovoltaic installed capacity have grown rapidly, making renewable energy a critical pillar for building a new-type power system. However, their inherent intermittency, variability, and uncertainty pose significant challenges to the secure and stable operation of modern power systems. As renewable energy capacity continues to expand, regions rich in renewable resources increasingly coincide with areas characterized by relatively underdeveloped grid infrastructure. This has resulted in insufficient renewable energy accommodation on the generation side and inadequate transmission capacity within the grid. Consequently, the integration of renewable energy faces growing challenges, including spatial mismatches between generation and demand, insufficient system flexibility, and increasing pressure on grid security and reliability. This study presents a systematic review combined with a quantitative techno-economic assessment of electrochemical energy-storage deployment in renewable-rich and weak-grid regions. Three representative electrochemical energy storage technologies, namely lithium-ion batteries, sodium-ion batteries, and all-vanadium flow batteries, are quantitatively evaluated using technical indicators and levelized cost metrics, including the levelized cost of energy (LCOE) and levelized cost of storage (LCOS). The results show that lithium-ion batteries exhibit the best overall techno-economic performance, with LCOE/LCOS values of 670/440 CNY kWh⁻¹, compared with 660/840 CNY kWh⁻¹ for sodium-ion batteries and 690/490 CNY kWh⁻¹ for all-vanadium flow batteries. Additionally, a 10%/2 h energy storage system improves day-ahead power prediction accuracy, ultra-short-term prediction accuracy, and the correlation coefficient by 67%, 44%, and 18%, respectively. These findings establish a quantitative basis for linking energy storage technology selection with regional resource–grid conditions and operational requirements, thereby supporting coordinated source-grid-load-storage planning and cost-effective and reliable renewable energy integration in weak-grid regions. Full article

Hyperlocal meteorological sensing is essential for accurate solar photovoltaic forecasting, yet professional-grade meteorological stations require investments easily exceeding $1000 USD per node, making distributed deployments economically inaccessible. This work presents a modular internet of things (IoT) device based on the ESP32 microcontroller integrating temperature, humidity, luminosity, and solar panel voltage sensing in an IP68-rated enclosure at a total hardware cost of about $65 USD when components are sourced in Germany. The enclosure-mounted temperature sensor is subject to a daytime radiative-heating bias and is not a calibrated ambient-air measurement. A hybrid architecture decouples external model training, performed on a conventional computer using the software Python and the open-source library TensorFlow, from autonomous on-device inference: every 15 min, the embedded feedforward network produces a single one-step-ahead (15 min) prediction of solar panel voltage from the most recent 96 real sensor readings; the resulting sequence of 96 such predictions, logged and assembled over a full day, forms the diurnal forecast profile reported below. This is executed via a three-layer feedforward network with 3011 parameters (11.8 KB). The network is trained offline on site-collected data and deployed on the microcontroller as static weight matrices without cloud connectivity. An on-device incremental gradient descent mechanism enables model adaptation after deployment without external retraining. The system was evaluated through two field deployments: a short period of hardware and firmware validation in Ulm, Germany, and a 115-day deployment in Zapopan, Mexico, comprising 84 days of training and 31 days of autonomous operation with zero missing records (no 15 min interval failed to log a reading); a real-time-clock fault affecting the final three validation days is addressed separately below and excluded from the reported metrics. Over a clean 28-day daytime window, the embedded model attained a coefficient of determination of 0.9165 and a mean absolute error of 0.2975 V (4.65% of the operational range), outperforming a climatology baseline (skill score 0.64) while not surpassing a 24 h persistence baseline. A frozen-weight ablation confirms that the on-device update mechanism yields a small but statistically significant accuracy gain ($p=0.001$), showing that autonomous incremental learning is implementable on low-cost hardware and produces a measurable effect, without cloud connectivity. Full article

As transistor scaling approaches physical and economic limits, advanced packaging has become an important approach to continued system scaling. This review compares two-dimensional (2D), two- and-a-half-dimensional (2.5D), and three-dimensional (3D) integration technologies, including silicon interposers, localized silicon bridges, redistribution layer (RDL) fan-out platforms, and vertical die stacking. The comparison focuses on interconnect geometry, bandwidth, energy efficiency, thermal and mechanical constraints, manufacturing maturity, cost, and major failure mechanisms. Representative applications in power electronics, high-performance computing (HPC), artificial intelligence (AI), radio frequency (RF) systems, and micro-electromechanical systems (MEMS) are discussed together with their packaging requirements. The relationships between package structure and thermal, mechanical, and electrical reliability are also examined. Emerging technologies, including vertical power delivery, glass substrates, hybrid bonding, and AI-assisted multiphysics design, are further discussed in terms of their role in future heterogeneous integration. Finally, a near-, medium-, and long-term roadmap is presented to summarize the main scaling targets and qualification requirements for larger, denser, and higher-power integrated systems. Full article

Fish processing and aquaculture wastes are protein- and lipid-rich by-products that can be used to produce renewable energy. However, evidence on their anaerobic valorization is limited. This bibliometric and systematic review maps the field and synthesizes evidence on the anaerobic digestion and dark fermentation of fish-derived waste. Scopus records from 2000 to 2025 were screened according to the PRISMA 2020 guidelines. A total of 164 articles comprised the bibliometric corpus and 120 research articles informed the qualitative synthesis. The annual publication growth rate was 13.29%, with 65% of publications occurring between 2019 and 2025. Most described experiments employed laboratory-scale batch assays, mesophilic conditions and co-digestion. Methane yields from fish offal, silage and recirculating aquaculture system sludge ranged from 48 to 1174 mL CH₄/g VS. These differences reflect variations in feedstock composition and preparation, proportion of fish waste, selection of co-substrates and operating conditions. The process performance was mainly constrained by ammonia, volatile fatty acids and long-chain fatty acids. The modified Gompertz model predominated, whereas multi-step dynamic modeling remained rare. Microbial studies, primarily 16S rRNA gene surveys conducted in a batch-based manner, linked fish waste digestion to bacteria that degrade proteins and lipids, as well as hydrogenotrophic methanogens. However, community responses depended on the composition of the feedstock, inoculum and loading rate. Only three dark fermentation studies were identified, two of which used fish-derived substrates. Overall, progress toward industrial implementation requires fraction-specific characterization, validation in continuous systems, integration of hydrogen and methane production, dynamic modeling, multi-omics, digestate-safety assessment and integrated techno-economic and life cycle assessment based on pilot- and industrial-scale data. To assess potential database coverage bias, the search was repeated in Scopus and an equivalent search was run in Web of Science. Five additional eligible studies were identified. Full article

Against the backdrop of the “dual-carbon” strategy (carbon peaking and carbon neutrality), countries worldwide are committed to advancing the application of new energy in the transportation sector. This has spurred the rapid development of electric vehicles (EVs) and led to higher requirements for the research and construction of charging infrastructure. To address the challenges of high daily power purchase costs and severe grid-connected power fluctuations in the daily scheduling of PV–storage–charging integrated stations, as well as the limitations of conventional particle swarm optimization (PSO) with random or chaotic initialization—including insufficient engineering prior knowledge of station time-of-use (TOU) electricity prices and energy storage state of charge (SOC), numerous inferior solutions in the initial population, and high susceptibility to premature convergence—this paper develops a dual-objective optimal scheduling model that balances daily power purchase cost and grid-connected power fluctuation. The model integrates PV output, EV charging loads, energy storage charge–discharge schedules, and multiple categories of operational constraints. Grounded in the economic operation principle of “valley-period charging and peak-period discharging”, an improved PSO algorithm with electricity PriceSOC joint guided initialization (PriceSOC-PSO) is proposed. High-quality initial particles are generated by setting segmented SOC targets, introducing random perturbations, and implementing closed-loop correction of the energy storage schedule, while hybrid random particles are incorporated into the population to preserve diversity. Multiple simulation scenarios, including the no-energy-storage case, standard PSO, chaotic-initialized PSO, the proposed PriceSOC-PSO, Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), and the Sparrow Search Algorithm (SSA), are established to carry out objective weight sensitivity analysis and cross-algorithm comparative analysis. The results demonstrate that, compared with the no-energy-storage scenario, the proposed strategy reduces the daily power purchase cost and grid-connected power fluctuation by 11.7% and 74.9% respectively under the weight configuration $({\omega }_1=0.3, {\omega }_2=0.7)$. When the weight configuration is adjusted to (${\omega }_1=0.7, {\omega }_2=0.3)$, the two indicators are decreased by 14.8% and 62.6% respectively. Full article

This study examines the long- and short-run relationships between energy use, renewable energy, education expenditure, female labor-force participation, economic development, and carbon dioxide (CO₂) emissions in the former BRICS economies—Brazil, Russia, India, China, and South Africa (BRICS-5)—over the period 1998–2020. A panel autoregressive distributed lag (ARDL) framework and error-correction specification are employed, complemented by alternative estimators and robustness analyses to account for cross-country heterogeneity and common shocks. The results indicate significant error-correcting behavior at the panel level, although adjustment dynamics and long-run relationships vary considerably across countries and estimators. In the baseline long-run estimates, energy use is positively associated with CO₂ emissions, whereas use of renewable energy and female labor-force participation are negatively associated with emissions. However, the positive energy-use association is estimator-sensitive and is not reproduced by the mean group and pooled mean group estimates. In contrast, education expenditure and GDP per capita do not exhibit statistically robust long-run associations with emissions. Comparisons across dynamic fixed effects, mean group, and pooled mean group approaches, together with specifications addressing common cross-sectional shocks, further demonstrate the importance of allowing for heterogeneity within the BRICS-5 group. Additional analysis provides no robust evidence supporting a common Environmental Kuznets Curve relationship. Overall, the findings suggest that energy structure and broader socioeconomic participation are relevant to emissions dynamics, but these relationships should not be interpreted as uniform across BRICS-5 economies. The results highlight the need for country-sensitive rather than homogeneous sustainability strategies. Full article

The rising demand for low-carbon hydrogen has intensified interest in biogas-reforming as a renewable, decentralized alternative to fossil-based production. However, published assessments of the competing reforming routes rely on inconsistent system boundaries and assumptions, which prevents a reliable comparison between them. This study addresses that gap by evaluating four biogas-reforming pathways, steam reforming (SR), dry reforming (DR), partial oxidation (POX), and autothermal reforming (ATR), within a single, consistent techno-economic and environmental assessment framework. Thermodynamic performance, life-cycle global warming potential, and the levelized cost of hydrogen (LCOH) are analyzed together, with cost uncertainty quantified through a 10,000-iteration Monte Carlo simulation and a six-parameter sensitivity analysis. The harmonized comparison shows that steam reforming is simultaneously the most favorable route for hydrogen yield, energy efficiency, levelized cost, and life-cycle carbon intensity, establishing it as the benchmark, whereas dry reforming, although it uniquely consumes CO₂, incurs the highest cost and the greatest catalyst-deactivation risk. Across all pathways, plant scale and capacity factor emerge as the dominant cost drivers, and biogas-derived hydrogen remains more expensive than conventional gray hydrogen, so its deployment depends on impurity-tolerant catalysts, improved heat integration, life-cycle-verified CO₂ management, and supportive low-carbon incentives. The novelty of this work lies in its unified, multi-criteria framework, which enables a like-for-like ranking of biogas-reforming routes and clarifies the conditions under which each becomes competitive. Full article

The industrial scalability and economic competitiveness of biomass-to-bioenergy and biorefinery systems depend on reliable feedstock supply, consistent biomass quality, and efficient logistics. An aspect that remains underexplored in biomass-to-biorefinery supply chain optimization is the incorporation of biomass quality uncertainty into decision-making models. Biomass quality characteristics, including ash content, moisture, chemical composition, and dry matter loss, can influence storage, preprocessing, transportation, conversion efficiency, biorefinery yields, process reliability, and overall energy utilization. Although these characteristics are difficult to model due to their spatial, temporal, and operational variability, ignoring their effects can lead to suboptimal supply-chain designs, inaccurate cost estimates, and unrealistic assessments of biorefinery performance. This paper reviews the treatment of biomass quality characteristics in the literature on quantitative modeling and analysis of biomass-to-biorefinery supply chains. Positioned from an Operational Research (OR) perspective, this review emphasizes mathematical modeling, computer simulation, optimization, and decision-support approaches for biomass-to-bioenergy systems. A total of 71 English-language published articles are reviewed and classified according to modeling approach and quality characteristic(s) considered. Across the selected literature that quantified biomass quality effects, cost reductions along supply chain operations ranging from 6% to 31% were reported when quality-aware models were compared with approaches that ignored quality or assumed unrealistic biomass quality characteristics. Despite these findings, biomass quality remains underrepresented in current analytical models; ash content, dry matter loss, and chemical composition were considered in only 10.4%, 4.3%, and 0.9% of the reviewed literature, respectively. This review summarizes the current state of research and outlines a future research agenda for integrating biomass quality control, uncertainty modeling, and optimization into scalable bioenergy and biorefinery systems. Full article

The flexible DC transmission scheme based on modular multilevel converters (MMCs) is currently the mainstream solution for offshore wind power delivery. With the ongoing growth in both installed capacity and the offshore distance of wind power projects, the dimensions and weight of corresponding offshore converter stations have increased substantially. These developments present significant economic constraints and engineering challenges, thereby complicating the deployment of large-scale, long-distance offshore wind energy systems. Compared with MMCs, diode rectifier units (DRUs) offer advantages such as compact size, light weight, low cost, reduced operating losses, and high reliability. Nevertheless, DRUs lack active control capability, and conventional grid following wind turbines cannot independently support the voltage of the offshore AC network, which severely limits their application in offshore wind scenarios with stringent economic requirements. Grid forming control of wind turbines is an effective approach to address this issue. Given the widespread application of doubly-fed induction generators (DFIGs) in engineering practice and their relatively low capital costs, this study investigates the implementation of grid-forming control strategies in DFIGs to address the stability challenges of DRU-based HVDC transmission systems during fault conditions. First, the mathematical model of the DFIG is established. Then, a suitable control strategy is designed to endow the turbine with certain grid forming capabilities. Finally, the developed simulation model and control strategy are verified in PSCAD/EMTDC. The results demonstrate that the proposed grid forming DFIG control strategy can maintain stable offshore AC voltage and frequency under various fault conditions, ensure continuous and reliable operation of the DRU, and achieve fault ride through. On this basis, to account for engineering practicality and cost considerations, this study further proposes a hybrid transmission scheme combining grid-following and grid-forming DFIGs. Simulation results confirm that this hybrid scheme also achieves satisfactory operational performance, while reducing the potential cost increase associated with full-scale grid-forming retrofits, it effectively ensures fault ride-through capability and system operational stability. This method provides an effective solution for low cost, highly reliable offshore wind power DC transmission. Full article

High wind-power penetration increases balancing requirements in integrated energy systems (IESs), while solvent-storage-assisted carbon capture power plants (CCPPs) and electric vehicle (EV) aggregators provide complementary flexibility at different timescales. This paper proposes an electric–carbon dual time-shift coordinated dispatch approach coupling carbon-energy shifting with vehicle-to-grid (V2G) electrical-energy shifting. First, a reduced-order model represents the dominant thermal inertia and short-term response of solvent regeneration. Second, EV availability uncertainty is characterized by Monte Carlo sampling, with quantile-based power and mobility-energy envelopes incorporated into aggregate SOC and mobility constraints together with a throughput-based battery-degradation cost. Finally, a 15-min mixed-integer linear programming model integrating power-to-gas, hydrogen-blended combined heat and power, thermal storage, and tiered carbon trading is solved using CPLEX. Compared with the baseline, the proposed coordinated dispatch strategy reduces operating cost from USD 77.19 × 10⁴ to 58.65 × 10⁴, net carbon emissions from 5841.71 to 2742.46 tCO₂, and the wind-curtailment rate from 42.98% to 1.15%. Specifically, relative to the same system without EV–V2G coordination, incorporating EV–V2G further reduces operating cost and net carbon emissions by 0.93% and 3.93%, respectively, while lowering the wind-curtailment rate from 4.23% to 1.15%, corresponding to a 72.8% relative reduction. Frequency-band analysis shows that the CCPP and electrolyzer provide the two largest contributions to low-frequency balancing, at 42.85% and 30.02%, respectively, whereas EV–V2G and CHP provide the two largest contributions to higher-frequency balancing, at 45.37% and 23.71%, respectively. The main limitations are the reduced-order regenerator model, fleet-level EV aggregation without distribution-network constraints, and fixed equipment capacities. Full article

Sperm cryopreservation is crucial for artificial propagation and the long-term preservation of valuable germplasm resources. This study aimed to establish an effective ultra-low temperature sperm cryopreservation protocol for Hefang bream (HFB), a novel hybrid bream variety developed through distant hybridization between blunt snout bream (Megalobrama amblycephala) and topmouth culter (Culter alburnus) followed by two rounds of backcrossing. Different combinations of extenders and cryoprotectants were evaluated based on post-thaw sperm motility and motion parameters to identify the optimal cryoprotective formulation for HFB sperm, with untreated fresh sperm as the control. Fresh sperm exhibited a total motility (MOT) of 98.87 ± 1.40%, curvilinear velocity (VCL) of 118.87 ± 5.38 μm/s, straight-line velocity (VSL) of 86.18 ± 5.67 μm/s, and average path velocity (VAP) of 107.48 ± 4.23 μm/s. The optimal formulation consisted of D15 extender (composed of 8 g/L NaCl, 0.5 g/L KCl and 15 g/L glucose) supplemented with 10% dimethyl sulfoxide (DMSO). Using a fresh sperm-to-cryoprotective medium ratio of 1:5 and a stepwise cooling procedure, post-thaw MOT, VCL, VSL, and VAP were 43.77 ± 13.85%, 37.54 ± 3.06 μm/s, 29.82 ± 1.98 μm/s, and 31.99 ± 2.14 μm/s, respectively. Further analyses revealed that the plasma membrane integrity (PMI), DNA integrity (DI) and mitochondrial activity (MA) of cryopreserved sperm were 43.40 ± 2.01%, 57.10 ± 4.79%, and 42.00 ± 1.65%, respectively, all of which were significantly lower than those of fresh sperm (p < 0.05). Ultrastructural analyses using scanning electron microscopy (SEM) and transmission electron microscopy (TEM) demonstrated that cryopreserved spermatozoa exhibited structural abnormalities, including plasma membrane disruption, flagellar fragmentation, and mitochondrial damage. Artificial insemination experiments showed that the fertilization and hatching rates of the cryopreserved sperm group were 55.10 ± 3.30% and 81.50 ± 3.29%, respectively, indicating that the established protocol could effectively support artificial reproduction in HFB. Furthermore, proteomic analysis was performed to investigate cryopreservation-induced molecular damage, identifying 7 cryopreservation-associated leakage proteins that were mainly enriched in pathways related to energy metabolism, cytoskeletal organization, and oxidative stress response. This study establishes an effective sperm cryopreservation protocol for HFB and provides insights into the cellular and molecular mechanisms underlying cryopreservation-induced sperm damage, offering a valuable reference for the development of sperm cryopreservation technologies in other economically important fish species. Full article

Water scarcity and groundwater salinization constrain irrigated agriculture in the Jordan Valley. This study evaluates a small-scale photovoltaic-powered brackish-water reverse osmosis (RO) desalination system successively upgraded from single-stage to two-stage RO operation and enhanced with ultrafiltration (UF) pre-treatment. The novelty of the study lies in the integrated assessment of these successive system configurations using field-based operational data. A screening gate-to-gate life-cycle assessment (LCA) and levelized cost of water (LCOW) analysis were performed for a functional unit of 1 m³ of irrigation-quality permeate. Three successive configurations were compared: (i) the baseline single-stage RO system, (ii) a two-stage RO system in which the first-stage concentrate was re-pressurized and treated in a second RO stage, and (iii) a UF-assisted two-stage RO system operated at up to 80% recovery. The configurations were compared using project inventory data for feedwater quality, recovery rate, energy demand, chemical consumption, materials and capital costs. The estimated climate-change impact of the UF-assisted PV-RO configuration was 0.9–1.4 kg CO₂-eq/m³. This impact is comparatively low for brackish-water RO and reflects the use of PV electricity, although direct comparison with the literature values is limited by differences in LCA boundaries and assumptions. Recovery increased from 35% for single-stage RO to 60% for two-stage RO and 80% for UF-assisted two-stage RO, reducing brine generation from 1.86 to 0.25 m³/m³ permeate. Economically, increasing RO membrane capacity reduced LCOW from 1.28 to 0.86 JD/m³, whereas the UF-RO configuration increased LCOW to 1.40 JD/m³ because of added capital cost. The results indicate that high recovery and solar power can improve environmental performance, but UF adoption should be justified by reliability, water-quality and membrane-lifetime benefits. Full article

The high levels of greenhouse gas emissions from energy-intensive industries have created an urgent need for decarbonization. As major sources of pollution, industries are increasingly being forced to reconsider the fuels they use in their processes. Interest in hydrogen combustion, particularly in high-temperature applications, has grown due to its physical properties and the absence of carbon dioxide emissions. Despite its potential, hydrogen combustion presents technical challenges, such as flame stability, burner adjustment requirements, control of nitrogen oxides (NO_x) emissions and material compatibility. This review examines the use of hydrogen as a fuel in industrial furnaces along three complementary axes. First, it analyzes combustion fundamentals, blending limits and mitigation strategies, including oxy-fuel and MILD combustion, and assesses their deployment maturity across the steel, cement, glass, ceramics, and refining and chemicals sectors, together with economic and regulatory constraints. Second, it reviews advances in Computational Fluid Dynamics (CFD) modeling of hydrogen flames, addressing turbulence-chemistry interaction, reaction kinetics, radiative heat transfer, NO_x formation and model validation. Third, it surveys camera-based diagnostics combined with Artificial Intelligence and computer vision for flame monitoring and combustion optimization. By synthesizing recent literature, the review identifies the principal knowledge gaps, notably standardized CFD validation datasets and robust monitoring under industrial conditions, providing a critical reference for researchers and industry. Full article

Lignocellulosic biomass is the most abundant renewable organic resource on Earth and represents a sustainable feedstock for biogas production through anaerobic digestion. However, the complex association of lignin, cellulose, and hemicellulose restricts microbial access, slows hydrolysis, and ultimately constrains methane yields from this resource. While existing reviews assess biological pretreatment and bioaugmentation as discrete interventions, this review evaluates their integration as complementary strategies for overcoming the recalcitrance of lignocellulosic biomass. As mechanistically distinct interventions, biological pretreatment mitigates substrate recalcitrance through selective lignin modification and biomass deconstruction, whereas bioaugmentation strengthens microbial functionality by enriching specialised populations that enhance hydrolytic, fermentative, and methanogenic activity. Integrating these approaches may therefore constitute an environmentally sustainable, energy-efficient, and process-compatible alternative to conventional thermochemical pretreatments. Limited available evidence indicates that when integrated, these strategies can improve lignocellulose digestibility, enhance process stability, and increase biomethane production. However, due to limited studies that explicitly combine biological pretreatment and bioaugmentation within a single experimental framework, claims regarding sustainability, energy efficiency, or economic advantages should be treated cautiously unless supported by direct life-cycle or techno-economic evidence. Key knowledge gaps include the mechanistic basis of their interactions, optimisation of microbial consortia and operating conditions, long term process stability, and scalability under industrially relevant conditions. Future research should prioritise integrated, systems level investigations that link substrate transformation with microbial community dynamics and evaluate techno-economic feasibility at larger scales. Addressing these challenges will be critical for advancing sustainable industrial biogas production from lignocellulosic biomass. Full article